• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.211-216
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• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.126-131
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• 1998

In computer vision system of modern industry precise measuring has lots of dfficulties because of measurement error due to distortion phenomenon. Among the difficulties, the distortion of edge is regraded as a dominent problem. which is caused by the vlurred image. The blurred image apperar when camera can not discriminate its precise focus. So. it is very important to decide focus of lens and to develop algorithm in order to correct distortion phenomenon. Thus. discrimination criteria obtained by image information of precise focus must be fixed in advance. The gray level histogram of image acquired from blurred edge tends to show a uniform distribution. Bimodal intensity histogram is related with condition of focus, and it is possible to find good condition of focus by using bimodal histogram of entropy.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.98-105
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• 2024

This paper proposes a new approach with a distance-based regularization to the entropy applied to the NBV (Next-Best-View) selection with NeRF (Neural Radiance Fields). 3D reconstruction requires images from various viewpoints, and selecting where to capture these images is a highly complex problem. In a recent work, image acquisition was derived using NeRF's ray-based uncertainty. While this work was effective for evaluating candidate viewpoints at fixed distances from a camera to an object, it is limited when dealing with a range of candidate viewpoints at various distances, because it tends to favor selecting viewpoints at closer distances. Acquiring images from nearby viewpoints is beneficial for capturing surface details. However, with the limited number of images, its image selection is less overlapped and less frequently observed, so its reconstructed result is sensitive to noise and contains undesired artifacts. We propose a method that incorporates distance-based regularization into entropy, allowing us to acquire images at distances conducive to capturing both surface details without undesired noise and artifacts. Our experiments with synthetic images demonstrated that NeRF models with the proposed distance and entropy-based criteria achieved around 50 percent fewer reconstruction errors than the recent work.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.755-762
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• 2000

Entropy coders as a noiseless compression method are widely used as end-point compression for images so there have been many contributions to increase of entropy coder performance and to reduction of entropy coder complexity. In this paper, we propose some entropy coders based on binary forward classification (BFC). BFC requires overhead of classification but there is no change between the amount of input information and that of classified output information, which we prove this property in this paper. And using the proved property, we propose entropy coders which are Golomb-Rice coder after BFC (BFC+GR) and arithmetic coder with BFC(BFC+A). The proposed entropy decoders do not have further complexity Son BFC. Simulation results also show better performance than other entropy coders which have similar complexity to proposed coders.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.9-16
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• 2006

Many image and video compression algorithms work by splitting the image into blocks and producing variable-length code bits for each block data. If variable-length code data are transmitted consecutively over error-prone channel without any error protection technique, the receiving decoder cannot decode the stream properly. So the standard image and video compression algorithms insert some redundant information into the stream to provide some protection against channel errors. One of redundancies is resynchronization marker, which enables the decoder to restart the decoding process from a known state in the event of transmission errors, but its usage should be restricted not to consume bandwidth too much. The Error Resilient Entropy Code(EREC) is well blown method which can regain synchronization without any redundant information. It can work with the overall prefix codes, which many image compression methods use. This paper proposes EREREC method to improve FEREC(Fast Error-Resilient Entropy Coding). It first calculates initial searching position according to bit lengths of consecutive blocks. Second, initial offset is decided using statistical distribution of long and short blocks, and initial offset can be adjusted to insure all offset sequence values can be used. The proposed EREREC algorithm can speed up the construction of FEREC slots, and can improve the compressed image quality in the event of transmission errors. The simulation result shows that the quality of transmitted image is enhanced about $0.3{\sim}3.5dB$ compared with the existing FEREC when random channel error happens.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.292-298
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• 2017

The complexity of image compression algorithms has increased in order to improve image compression efficiency. One way to resolve high computational complexity is parallel processing. However, entropy coding, which is lossless compression, does not fit into the parallel processing form because of the correlation between consecutive symbols. This paper proposes a new application-specific instruction set processor (ASIP) platform by adding new context-adaptive binary arithmetic coding (CABAC) instructions to the existing platform to quickly process a variety of entropy coding. The newly added instructions work without conflicts with all other existing instructions of the platform, providing the flexibility to handle many coding standards with fast processing speeds. CABAC software is implemented for High Efficiency Video Coding (HEVC) and the performance of the proposed ASIP platform was verified with a field programmable gate array simulation.

• Journal of The Korean Astronomical Society
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• v.27 no.2
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• pp.191-201
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• 1994

We propose to use the entropy of power spectra defined in the frequency domain for the deconvolution of extended images. Spatial correlations requisite for extended sources may be insured by increasing the role of power entropy because the power is just a representation of spatial correlations in the frequency domain. We have derived a semi-analytical solution which is found to severely reduce computing time compared with other iteration schemes. Even though the solution is very similar to the well-known Wiener filter, the regularizingng term in the new expression is so insensitive to the noise characteristics as to assure a stable solution. Applications have been made to the IRAS $60{\mu}m\;and\;100{\mu}m$ images of the dark cloud B34 and the optical CCD image of a solar active region containing a circular sunspot and a small pore.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1515-1522
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• 1997

Several image coding algorithms have been developed for the telecommunication and multimedia systems with high image quality and high compression ratio. In order to achieve low entropy and distortion, the system should pay great cost of computation time and memory. In this paper, the uniform cubic lattice is chosen for Lattice Vector Quantization (LVQ) because of its generic simplicity. As a transform coding, the Discrete Wavelet Transform (DWT) is applied to the images because of its multiresolution property. The proposed algorithm is basically composed of the biorthogonal DWT and the uniform cubic LVQ. The multiresolution property of the DWT is actively used to optimize the entropy and the distortion on the basis of the distortion-rate function. The vector codebooks are also designed to be optimal at each subimage which is analyzed by the biorthogonal DWT. For compression efficiency, the vector codebook has different dimension depending on the variance of subimage. The simulation results show that the performance of the proposed coding mdthod is superior to the others in terms of the computation complexity and the PSNR in the range of entropy below 0.25 bpp.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.587-594
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• 2005

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.